Determining where bottlenecks occur in multi-threaded multi-path computing systems

ABSTRACT

One or more processors analyze interaction data for two or more nodes within a plurality of nodes processing a computing transaction. One or more processors determine a number of inbound requests and a number of outbound requests for the two or more nodes within the plurality of nodes. One or more processors determine whether one or more nodes within the plurality of nodes are limiting computing performance.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of applicationperformance management (APM), and more particularly to determining wherebottlenecks occur along transaction pathways.

APM is focused on monitoring applications and application environmentsto determine application health and efficiency. APM is used to monitorand predict performance issues and other application problems either inthe application or in the environment in which the application runs.

APM software collects many metrics from the application environmentincluding central processing unit (CPU) usage, memory usage,transaction, latency, transaction rates, interaction data (correlationdata), response times, thread pool usage, garbage collection (GC)activity, etc. Large amounts of data are collected from many complexapplication environments. More and more software vendors and customersare focusing on analytics to uncover insights from the availableapplication performance data.

When transaction response time is identified as excessive, APM attemptsto isolate the cause or at least provide users with an indication or“best guess” as to the probable cause and where to focus on a problemresolution.

SUMMARY

Embodiments of the present invention provide a method, system, andprogram product to determine where bottlenecks occur in multi-threadedmulti-path computing systems. One or more processors analyze interactiondata for two or more nodes within a plurality of nodes processing acomputing transaction. One or more processors determine a number ofinbound requests and a number of outbound requests for the two or morenodes within the plurality of nodes. One or more processors determinewhether one or more nodes within the plurality of nodes are limitingcomputing performance.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a multi-threadedmulti-path computing environment, in accordance with an exemplaryembodiment of the present invention;

FIG. 2 illustrates operational processes for determining whether one ormore nodes are limiting computing performance on one or more computingdevices within the environment of FIG. 1, in accordance with anexemplary embodiment of the present invention;

FIG. 3 depicts a first example for the determination of node loadcoefficients by a load coefficient program, in accordance with anexemplary embodiment of the present invention;

FIG. 4 depicts a second example for the determination of node loadcoefficients by a load coefficient program, in accordance with anexemplary embodiment of the present invention;

FIG. 5 depicts a third example for the determination of node loadcoefficients by a load coefficient program, in accordance with anexemplary embodiment of the present invention; and

FIG. 6 depicts a block diagram of components of the computing deviceexecuting software, in accordance with an exemplary embodiment of thepresent invention.

DETAILED DESCRIPTION

Embodiments of the present invention recognize that slow response timesin a multi-threaded multi-path computing environment are often caused byone or more bottlenecks involving one or more nodes in the computingsystem. Embodiments of the present invention provide a method, system,and program product for analyzing inbound and outbound calls at nodes ina multi-threaded multi-path computing environment and determiningwhether one or more nodes are causing a slow response time.

In a complex multi-threaded multi-path computing environment, where atransaction is distributed across multiple domains and the interactionsbetween the domains occur in parallel, it is difficult to determine thecause of high transaction response times. The problem may be caused byany of the nodes traversed by the application and since many nodes havemultiple moving parts, there are many potential trouble spots withineach node. As used herein, a “node” includes servers, middle-ware, andapplication instances.

A simple example is a 3-tier web application: The user interacts with abrowser, which in turn accesses a web server. The web server then sendsa request to a back-end application server, which in turn accesses adatabase server. In this example, the end-user response time may bedelayed by a slow web server, a slow application server, or a slowdatabase server. The slowdown may be caused by insufficient resourcessuch as poor CPU performance, lack of sufficient disk space on one ormore of the servers, insufficient servers, not enough threads availableto dispatch incoming work, or other causes.

Response times are often delayed by a single node causing a bottleneckthereby limiting overall computing performance. For example, a slowdatabase server will cause the application server to wait which in turnwill cause the web server to wait. Response times for all three nodesare affected in this example, but the root cause is the database server.A challenge for APM software is to determine the root causes of suchdelays. This is done by understanding relationships between all themonitoring data collected from the various systems. APM softwareincludes transaction tracking software. Two types of data collected bytransaction tracking software include resource data and interactiondata. Resource data includes CPU, memory and disk usage data, etc. Theinteraction data provides information showing how nodes communicate toeach other, which includes data detailing the timing and direction ofrequest/response pairs.

The present invention will now be described in detail with reference tothe Figures.

FIG. 1 is a functional block diagram illustrating a multi-threadedmulti-path computing environment, generally designated 100, inaccordance with one embodiment of the present invention. Computingenvironment 100 includes computing devices 102, 106, 110, 116, and 124connected over network 114. Computing devices 102, 106, and 110 includenodes 104, 108, and 112, respectively. APM computing device 116 includesload coefficient program 118, transaction tracking software 120, andinteraction data 122. Client computing device 124 includes clientprogram 126.

In various embodiments of the present invention, computing devices 102,106, 110, 116, and 124 are computing devices that can be standalonedevices, servers, laptop computers, tablet computers, netbook computers,personal computers (PCs), or desktop computers. In another embodiment,computing devices 102, 106, 110, 116, and 124 represent a computingsystem utilizing clustered computers and components to act as a singlepool of seamless resources. In general, computing devices 102, 106, 110,116, and 124 can be any single computing device or a combination ofdevices with access to nodes 104, 108, and 112, transaction trackingsoftware 120, interaction data 122 and is capable of executing loadcoefficient program 118 and client program 126. Computing devices 102,106, 110, 116, and 124 may include internal and external hardwarecomponents, as depicted and described in further detail with respect toFIG. 6.

In this exemplary embodiment, software and data components of nodes 104,108, and 112 are stored on computing devices 102, 106, and 110,respectively, load coefficient program 118, transaction trackingsoftware 120, and interaction data 122 are stored on APM computingdevice 116, and client program 126 is stored on client computing device124. However, in other embodiments, the software and data elementslisted above may be stored externally and accessed through acommunication network, such as network 114. Network 114 can be, forexample, a local area network (LAN), a wide area network (WAN) such asthe Internet, or a combination of the two, and may include wired,wireless, fiber optic or any other connection known in the art. Ingeneral, network 114 can be any combination of connections and protocolsthat will support communications between the software, hardware, anddata components discussed above, in accordance with a desired embodimentof the present invention.

In exemplary embodiments, nodes 104, 108, and 112 include hardware,software, and data components of computing devices 102, 106, and 110,respectively. For example, nodes such as nodes 104, 108, and 112 includeservers, middleware, and application instances in various embodiments.In general, nodes 104, 108, and 112 are components of one or moremulti-threaded multi-path computing environments and the interactions ofnodes 104, 108, and 112 when performing operations on clienttransactions are recorded by transaction tracking software 120 andstored into interaction data 122.

In exemplary embodiments, load coefficient program 118 retrievestimestamped data from interaction data 122 detailing the timing andnumber of inbound and outbound transactions that traverse nodes such asnodes 104, 108, and 112. Based on the data retrieved from interactiondata 122, load coefficient program 118 calculates load coefficients fornodes such as nodes 104, 108, and 112. In various embodiments, the loadcoefficients calculated by load coefficient program 118 are values thatrange from zero to one and indicate which nodes are likely to bebottlenecks limiting computing performance. In other embodiments, theload coefficients calculated by load coefficient program 118 arepercentages ranging from zero percent to one hundred percent againindicating which nodes are likely to be bottlenecks limiting computingperformance. Regardless of how the load coefficients are expressed, theyrepresent the ratio of incoming and outgoing transactions at a node fora determined time window.

In exemplary embodiments, client program 126 is an application thatgenerates transactions, which are operated on by nodes 104, 108, and112. As described above, one example of a client program such as clientprogram 126 is a web browser that generates transactions to be operatedon by nodes such as a web server, application server, and databaseserver.

FIG. 2 illustrates operational processes for determining whether one ormore nodes (i.e., nodes 104, 108, and 112) are limiting computingperformance on one or more computing devices (i.e., computing devices102, 106, and 110) within the environment of FIG. 1, in accordance withan exemplary embodiment of the present invention.

In step 202, interaction data 122 is collected for nodes 104, 108, and112 by transaction tracking software 120. Interaction data 122 includesinformation showing how nodes 104, 108, and 112 communicate to eachother, which includes data detailing the timing and direction oftransaction request/response pairs originating from client program 126.

In step 204, load coefficient program 118 determines a time window for atransaction generated by client program 126. In various embodiments, thetime window for the transaction is determined based on transactionresponse times, which are included in interaction data 122 or otherwiseobtainable from transaction tracking software 120. In other embodiments,the time window is a user-determined period of time or a standard lengthof time designated by hard-coded instructions within load coefficientprogram 118.

In step 206, load coefficient program 118 analyzes interaction data 122for nodes 104, 108, and 112 during the time window determined in step204. The interaction data includes the times inbound requests werereceived and outbound requests were sent at each node for instances oftransactions originating from client program 126.

In step 208, load coefficient program 118 determines the number ofinbound requests and outbound requests at each of nodes 104, 108, and112 for instances of transactions originating from client program 126.The inbound and outbound requests are requests that are time-stampedwith times that fall within the time window determined in step 204.

In step 210, load coefficient program 118 determines whether one or moreof nodes 104, 108, and 112 are limiting computing performance. Invarious embodiments, a lower number of inbound/outbound requests at asecond node relative to an immediately upstream first node indicatesqueuing at the second node. For each instance of a transaction, anoperation is performed at each node. The inbound request occurs at thenode before the operation and is time-stamped. After the operationoccurs, that node sends an outbound request to the next downstream node,which is also time-stamped. An outbound request from a node signifiesthat the operation for which that node was responsible was successfullyprocessed. If a downstream node has been sent more outbound requeststhan it has processed, then that is an indication that the downstreamnode is processing its operation slower than the node immediatelyupstream from it.

FIG. 3 depicts a first example for the determination of node loadcoefficients by load coefficient program 118, in accordance with anexemplary embodiment of the present invention.

Transactions 302-316 represent several transactions competing for theresources of nodes 104, 108, and 112. In this and subsequent examples,the transactions depicted proceed through nodes 104, 106, and 112 insuccession. In this schematic, the presence of a transaction at a nodeindicates that the transaction has been detected inbound at the node.Thus, transactions 302 and 304 have been detected inbound by all threenodes. Transactions 306-316, however, have only been detected inbound bynodes 104 and 108 and not node 112. Transactions 306-316 have beendetected outbound from node 108 (a shortened arrow only indicatesoutbound from the node in which the arrow originates from and a longarrow indicates outbound from the node in which the arrow originatesfrom and inbound to the node in which the arrow terminates).

Based on the above data, load coefficient program 118 calculates loadcoefficients for all three nodes. In this embodiment, load coefficientsare the ratio of transactions detected inbound at a node andtransactions detected outbound from an immediate upstream node. In thisexample, node 104 is the first node encountered by the transactions so aload coefficient equal to one indicates that only eight transactionshave been sent by client program 126. Node 108 has a load coefficientequal to one because all eight of the transactions detected outboundfrom node 104 have been detected inbound at node 108 (8/8=1). However,node 112 has a load coefficient equal to 0.25 because only two of theeight transactions that are detected outbound from node 108 are detectedinbound at node 112 (2/8=0.25). This indicates that queuing oftransactions 306-316 is occurring at node 112, which in turn indicatesthat node 112 is a bottleneck that is limiting overall computingperformance.

FIG. 4 depicts a second example for the determination of node loadcoefficients by load coefficient program 118, in accordance with anexemplary embodiment of the present invention.

In this example, transactions 402-416 represent several transactionscompeting for the resources of nodes 104, 108, and 112. Transactions 402and 404 have been detected inbound at all three nodes. Transactions406-416, however, have only been detected inbound at node 104, but notnodes 108 and 112.

Based on the above data, load coefficient program 118 calculates loadcoefficients for all three nodes. Like the previous example, node 104 isthe first node encountered by the transactions so a load coefficientequal to one indicates that only eight transactions have been sent byclient program 126. Node 108 has a load coefficient equal to 0.25because only two of the eight transactions that are detected outboundfrom node 104 are detected inbound at node 108. This indicates thatqueuing of transactions 406-416 is occurring at node 108, which in turnindicates that node 108 is a bottleneck that is limiting overallcomputing performance. In this example, node 112 has a load coefficientequal to one because both transactions detected as outbound at node 108are detected as inbound at node 112. This indicates that node 112 is nota bottleneck limiting overall computing performance although it saysnothing regarding whether node 112 would be a bottleneck if node 108 wasnot a bottleneck.

FIG. 5 depicts a third example for the determination of node loadcoefficients by load coefficient program 118, in accordance with anexemplary embodiment of the present invention.

In this example, transactions 502-516 represent several transactionscompeting for the resources of nodes 104, 108, and 112. Transactions 502and 504 have been detected inbound at all three nodes. Transaction 506,however, has only been detected inbound at nodes 104 and 108, but notnode 112. Further, transactions 508-516, which are detected inbound atnode 104, are not detected inbound at nodes 108 and 112.

Based on the above data, load coefficient program 118 calculates loadcoefficients for all three nodes. Like the previous example, node 104 isthe first node encountered by the transactions so a load coefficientequal to one indicates that only eight transactions have been sent byclient program 126. Node 108 has a load coefficient equal to 0.38because only three of the eight transactions that are detected outboundfrom node 104 are detected inbound at node 108. This indicates thatqueuing of transactions 508-516 is occurring at node 108, which in turnindicates that node 108 is a bottleneck that is limiting overallcomputing performance. In this example, node 112 has a load coefficientequal to 0.67 because only two of the three transactions detected asoutbound at node 108 are detected as inbound at node 112 (2/3=0.67).This indicates that node 112 is also a bottleneck limiting overallcomputing performance.

It should be understood that expressing load coefficients in a range ofzero to one is just one possible way of expressing this data. There aremany other possible ways to express load coefficients that would beapparent to one skilled in the art. For example, load coefficients arealso capable of being expressed as percentages, i.e., a load coefficientequal to one is also capable of being expressed as one hundred percent.

In various embodiments, load coefficient program 118 prioritizes nodesrequiring service because of bottlenecking. In one embodiment, loadcoefficient program 118 prioritizes nodes for service based on the valueof the corresponding load coefficients. Thus, for the example in FIG. 5,load coefficient program 118 prioritizes node 108 for service over node112 because the load coefficient for node 108 is lower than the loadcoefficient for node 112 (0.38 and 0.67, respectively). In anotherembodiment, load coefficient program 118 prioritizes nodes for servicebased on the number of transactions detected inbound. Thus, for theexample in FIG. 5, load coefficient program 118 prioritizes node 112 forservice over node 108 because the number of inbound transactions is lessfor node 112 compared to node 108 (2 and 3, respectively). In yetanother embodiment, load coefficient program 118 provides the raw datato a user and the user prioritizes bottlenecking nodes for service.

FIG. 6 depicts a block diagram, 600, of components of computing devicesused to practice embodiments of the present invention (e.g., computingdevices 102, 106, 110, 116, and 124), in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.6 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

Computing devices used to practice embodiments of the present inventioninclude communications fabric 602, which provides communications betweencomputer processor(s) 604, memory 606, persistent storage 608,communications unit 610, and input/output (I/O) interface(s) 612.Communications fabric 602 can be implemented with any architecturedesigned for passing data and/or control information between processors(such as microprocessors, communications and network processors, etc.),system memory, peripheral devices, and any other hardware componentswithin a system. For example, communications fabric 602 can beimplemented with one or more buses.

Memory 606 and persistent storage 608 are computer-readable storagemedia. In this embodiment, memory 606 includes random access memory(RAM) 614 and cache memory 616. In general, memory 606 can include anysuitable volatile or non-volatile computer-readable storage media.

Software and data 601 used to practice embodiments of the presentinvention (e.g., software and data components of nodes 104, 108, and112, load coefficient program 118, transaction tracking software 120,interaction data 122, and client program 126) are stored in persistentstorage 608 for execution and/or access by one or more of the respectivecomputer processors 604 via one or more memories of memory 606. In thisembodiment, persistent storage 608 includes a magnetic hard disk drive.Alternatively, or in addition to a magnetic hard disk drive, persistentstorage 608 can include a solid state hard drive, a semiconductorstorage device, read-only memory (ROM), erasable programmable read-onlymemory (EPROM), flash memory, or any other computer-readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 608 may also be removable. Forexample, a removable hard drive may be used for persistent storage 608.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage608.

Communications unit 610, in these examples, provides for communicationswith other data processing systems or devices, including resources ofnetwork 114. In these examples, communications unit 610 includes one ormore network interface cards. Communications unit 610 may providecommunications through the use of either or both physical and wirelesscommunications links. Software and data 601 used to practice embodimentsof the present invention may be downloaded to persistent storage 608through communications unit 610.

I/O interface(s) 612 allows for input and output of data with otherdevices that may be connected to computing devices used to practiceembodiments of the present invention. For example, I/O interface 612 mayprovide a connection to external devices 618 such as a keyboard, keypad,a touch screen, and/or some other suitable input device. Externaldevices 618 can also include portable computer-readable storage mediasuch as, for example, thumb drives, portable optical or magnetic disks,and memory cards. Software and data used to practice embodiments of thepresent invention can be stored on such portable computer-readablestorage media and can be loaded onto persistent storage 608 via I/Ointerface(s) 612. I/O interface(s) 612 also connect to a display 620.

Display 620 provides a mechanism to display data to a user and may be,for example, a computer monitor, or a television screen.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

It is to be noted that the term(s) such as “Smalltalk” and the like maybe subject to trademark rights in various jurisdictions throughout theworld and are used here only in reference to the products or servicesproperly denominated by the marks to the extent that such trademarkrights may exist.

What is claimed is:
 1. A method comprising: analyzing, by one or moreprocessors, interaction data for two or more nodes within a plurality ofnodes processing a computing transaction; determining, by the one ormore processors, a number of inbound requests and a number of outboundrequests for the two or more nodes within the plurality of nodes;determining, by the one or more processors, a load coefficient for theone or more nodes within the plurality of nodes, wherein the loadcoefficient represents a ratio of inbound requests to outbound requestsand has one or both of: 1) a value less than or equal to one and greaterthan or equal to zero; and 2) a value less than or equal to one hundredpercent and greater than or equal to zero percent; and determining, bythe one or more processors, whether one or more nodes within theplurality of nodes are limiting computing performance.
 2. The method ofclaim 1, wherein the step of determining, by the one or more processors,a number of inbound requests and a number of outbound requests for thetwo or more nodes within the plurality of nodes further comprises:determining, by the one or more processors, a time window of atransaction; and determining, by the one or more processors, the numberof inbound requests and the number of outbound requests within the timewindow for the two or more nodes within the plurality of nodes.
 3. Themethod of claim 1 further comprising: determining, by the one or moreprocessors, that a node within the plurality of nodes is limitingcomputing performance based, at least in part, on the node having one orboth of: a load coefficient value less than one and a load coefficientvalue less than one hundred percent.
 4. The method of claim 1, whereinthe plurality of nodes includes one or more of: servers, middleware, andapplication instances.
 5. The method of claim 1, wherein the pluralityof nodes includes components of one or more multi-threaded multi-pathcomputing environments.
 6. The method of claim 1, wherein the pluralityof nodes includes one or more of: web servers, application servers, anddatabase servers.
 7. A computer program product comprising: one or morecomputer-readable storage media and program instructions stored on atleast one of the one or more computer-readable storage media, theprogram instructions comprising: program instructions to analyzeinteraction data for two or more nodes within a plurality of nodesprocessing a computing transaction; program instructions to determine anumber of inbound requests and a number of outbound requests for the twoor more nodes within the plurality of nodes; program instructions todetermine a load coefficient for the one or more nodes within theplurality of nodes, wherein the load coefficient represents a ratio ofinbound requests to outbound requests and has one or both of: 1) a valueless than or equal to one and greater than or equal to zero; and 2) avalue less than or equal to one hundred percent and greater than orequal to zero percent; and program instructions to determine whether oneor more nodes within the plurality of nodes are limiting computingperformance.
 8. The computer program product of claim 7, wherein theprogram instructions to determine a number of inbound requests and anumber of outbound requests for the two or more nodes within theplurality of nodes further comprises: program instructions to determinea time window of a transaction; and program instructions to determinethe number of inbound requests and the number of outbound requestswithin the time window for the two or more nodes within the plurality ofnodes.
 9. The computer program product of claim 7 further comprising:program instructions to determine that a node within the plurality ofnodes is limiting computing performance based, at least in part, on thenode having one or both of: a load coefficient value less than one and aload coefficient value less than one hundred percent.
 10. The computerprogram product of claim 7, wherein the plurality of nodes includes oneor more of: servers, middleware, and application instances.
 11. Thecomputer program product of claim 7, wherein the plurality of nodesincludes components of one or more multi-threaded multi-path computingenvironments.
 12. The computer program product of claim 7, wherein theplurality of nodes includes one or more of: web servers, applicationservers, and database servers.
 13. A computer system comprising: one ormore computer processors; one or more computer-readable storage media;and program instructions stored on at least one of the one or morecomputer-readable storage media for execution by at least one of the oneor more processors, the program instructions comprising: programinstructions to analyze interaction data for two or more nodes within aplurality of nodes processing a computing transaction; programinstructions to determine a number of inbound requests and a number ofoutbound requests for the two or more nodes within the plurality ofnodes; program instructions to determine a load coefficient for the oneor more nodes within the plurality of nodes, wherein the loadcoefficient represents a ratio of inbound requests to outbound requestsand has one or both of: 1) a value less than or equal to one and greaterthan or equal to zero; and 2) a value less than or equal to one hundredpercent and greater than or equal to zero percent; and programinstructions to determine whether one or more nodes within the pluralityof nodes are limiting computing performance.
 14. The computer system ofclaim 13, wherein the program instructions to determine a number ofinbound requests and a number of outbound requests for the two or morenodes within the plurality of nodes further comprises: programinstructions to determine a time window of a transaction; and programinstructions to determine the number of inbound requests and the numberof outbound requests within the time window for the two or more nodeswithin the plurality of nodes.
 15. The computer system of claim 13further comprising: program instructions to determine that a node withinthe plurality of nodes is limiting computing performance based, at leastin part, on the node having one or both of: a load coefficient valueless than one and a load coefficient value less than one hundredpercent.
 16. The computer system of claim 13, wherein the plurality ofnodes includes one or more of: servers, middleware, and applicationinstances.
 17. The computer system of claim 13, wherein the plurality ofnodes includes components of one or more multi-threaded multi-pathcomputing environments.